Overview

Dataset statistics

Number of variables31
Number of observations538837
Missing cells2713637
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory127.4 MiB
Average record size in memory248.0 B

Variable types

Numeric20
Categorical5
Text6

Alerts

CANCELLED is highly imbalanced (86.4%)Imbalance
DIVERTED is highly imbalanced (97.5%)Imbalance
DEP_TIME has 9978 (1.9%) missing valuesMissing
DEP_DELAY has 9982 (1.9%) missing valuesMissing
TAXI_OUT has 10197 (1.9%) missing valuesMissing
TAXI_IN has 10519 (2.0%) missing valuesMissing
ARR_TIME has 10519 (2.0%) missing valuesMissing
ARR_DELAY has 11640 (2.2%) missing valuesMissing
CANCELLATION_CODE has 528542 (98.1%) missing valuesMissing
AIR_TIME has 11640 (2.2%) missing valuesMissing
CARRIER_DELAY has 422124 (78.3%) missing valuesMissing
WEATHER_DELAY has 422124 (78.3%) missing valuesMissing
NAS_DELAY has 422124 (78.3%) missing valuesMissing
SECURITY_DELAY has 422124 (78.3%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 422124 (78.3%) missing valuesMissing
SECURITY_DELAY is highly skewed (γ1 = 34.44878616)Skewed
DEP_DELAY has 23846 (4.4%) zerosZeros
ARR_DELAY has 9345 (1.7%) zerosZeros
CARRIER_DELAY has 53559 (9.9%) zerosZeros
WEATHER_DELAY has 110206 (20.5%) zerosZeros
NAS_DELAY has 57001 (10.6%) zerosZeros
SECURITY_DELAY has 116087 (21.5%) zerosZeros
LATE_AIRCRAFT_DELAY has 62630 (11.6%) zerosZeros

Reproduction

Analysis started2024-03-30 05:35:14.686200
Analysis finished2024-03-30 05:38:07.899917
Duration2 minutes and 53.21 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8902692
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:08.053776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.07623
Coefficient of variation (CV)0.53369827
Kurtosis-1.3106465
Mean3.8902692
Median Absolute Deviation (MAD)2
Skewness0.09438584
Sum2096221
Variance4.3107308
MonotonicityIncreasing
2024-03-30T02:38:08.342760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 90875
16.9%
7 86695
16.1%
2 86270
16.0%
5 72554
13.5%
4 72392
13.4%
3 68901
12.8%
6 61150
11.3%
ValueCountFrequency (%)
1 90875
16.9%
2 86270
16.0%
3 68901
12.8%
4 72392
13.4%
5 72554
13.5%
6 61150
11.3%
7 86695
16.1%
ValueCountFrequency (%)
7 86695
16.1%
6 61150
11.3%
5 72554
13.5%
4 72392
13.4%
3 68901
12.8%
2 86270
16.0%
1 90875
16.9%

FL_DATE
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
1/27/2023 12:00:00 AM
 
18539
1/26/2023 12:00:00 AM
 
18519
1/20/2023 12:00:00 AM
 
18506
1/19/2023 12:00:00 AM
 
18472
1/13/2023 12:00:00 AM
 
18422
Other values (26)
446379 

Length

Max length21
Median length21
Mean length20.713631
Min length20

Characters and Unicode

Total characters11161271
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/2/2023 12:00:00 AM
2nd row1/2/2023 12:00:00 AM
3rd row1/2/2023 12:00:00 AM
4th row1/2/2023 12:00:00 AM
5th row1/2/2023 12:00:00 AM

Common Values

ValueCountFrequency (%)
1/27/2023 12:00:00 AM 18539
 
3.4%
1/26/2023 12:00:00 AM 18519
 
3.4%
1/20/2023 12:00:00 AM 18506
 
3.4%
1/19/2023 12:00:00 AM 18472
 
3.4%
1/13/2023 12:00:00 AM 18422
 
3.4%
1/12/2023 12:00:00 AM 18380
 
3.4%
1/9/2023 12:00:00 AM 18233
 
3.4%
1/30/2023 12:00:00 AM 18201
 
3.4%
1/23/2023 12:00:00 AM 18185
 
3.4%
1/16/2023 12:00:00 AM 18181
 
3.4%
Other values (21) 355199
65.9%

Length

2024-03-30T02:38:08.617486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00 538837
33.3%
am 538837
33.3%
1/27/2023 18539
 
1.1%
1/26/2023 18519
 
1.1%
1/20/2023 18506
 
1.1%
1/19/2023 18472
 
1.1%
1/13/2023 18422
 
1.1%
1/12/2023 18380
 
1.1%
1/9/2023 18233
 
1.1%
1/30/2023 18201
 
1.1%
Other values (23) 391565
24.2%

Most occurring characters

ValueCountFrequency (%)
0 2747960
24.6%
2 1846135
16.5%
1 1317349
11.8%
/ 1077674
 
9.7%
1077674
 
9.7%
: 1077674
 
9.7%
3 628509
 
5.6%
A 538837
 
4.8%
M 538837
 
4.8%
9 54785
 
0.5%
Other values (5) 255837
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11161271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2747960
24.6%
2 1846135
16.5%
1 1317349
11.8%
/ 1077674
 
9.7%
1077674
 
9.7%
: 1077674
 
9.7%
3 628509
 
5.6%
A 538837
 
4.8%
M 538837
 
4.8%
9 54785
 
0.5%
Other values (5) 255837
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11161271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2747960
24.6%
2 1846135
16.5%
1 1317349
11.8%
/ 1077674
 
9.7%
1077674
 
9.7%
: 1077674
 
9.7%
3 628509
 
5.6%
A 538837
 
4.8%
M 538837
 
4.8%
9 54785
 
0.5%
Other values (5) 255837
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11161271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2747960
24.6%
2 1846135
16.5%
1 1317349
11.8%
/ 1077674
 
9.7%
1077674
 
9.7%
: 1077674
 
9.7%
3 628509
 
5.6%
A 538837
 
4.8%
M 538837
 
4.8%
9 54785
 
0.5%
Other values (5) 255837
 
2.3%
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
WN
112430 
DL
75174 
AA
74999 
UA
56657 
OO
50347 
Other values (10)
169230 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1077674
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 112430
20.9%
DL 75174
14.0%
AA 74999
13.9%
UA 56657
10.5%
OO 50347
9.3%
YX 24476
 
4.5%
B6 23249
 
4.3%
NK 21876
 
4.1%
AS 19801
 
3.7%
MQ 18849
 
3.5%
Other values (5) 60979
11.3%

Length

2024-03-30T02:38:09.059843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 112430
20.9%
dl 75174
14.0%
aa 74999
13.9%
ua 56657
10.5%
oo 50347
9.3%
yx 24476
 
4.5%
b6 23249
 
4.3%
nk 21876
 
4.1%
as 19801
 
3.7%
mq 18849
 
3.5%
Other values (5) 60979
11.3%

Most occurring characters

ValueCountFrequency (%)
A 233153
21.6%
N 134306
12.5%
O 116150
10.8%
W 112430
10.4%
D 75174
 
7.0%
L 75174
 
7.0%
U 56657
 
5.3%
9 30211
 
2.8%
Y 24476
 
2.3%
X 24476
 
2.3%
Other values (11) 195467
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1077674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 233153
21.6%
N 134306
12.5%
O 116150
10.8%
W 112430
10.4%
D 75174
 
7.0%
L 75174
 
7.0%
U 56657
 
5.3%
9 30211
 
2.8%
Y 24476
 
2.3%
X 24476
 
2.3%
Other values (11) 195467
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1077674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 233153
21.6%
N 134306
12.5%
O 116150
10.8%
W 112430
10.4%
D 75174
 
7.0%
L 75174
 
7.0%
U 56657
 
5.3%
9 30211
 
2.8%
Y 24476
 
2.3%
X 24476
 
2.3%
Other values (11) 195467
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1077674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 233153
21.6%
N 134306
12.5%
O 116150
10.8%
W 112430
10.4%
D 75174
 
7.0%
L 75174
 
7.0%
U 56657
 
5.3%
9 30211
 
2.8%
Y 24476
 
2.3%
X 24476
 
2.3%
Other values (11) 195467
18.1%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5874
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2201.9296
Minimum1
Maximum9887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:09.365567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile266
Q1972
median1909
Q33056
95-th percentile5302
Maximum9887
Range9886
Interquartile range (IQR)2084

Descriptive statistics

Standard deviation1547.1982
Coefficient of variation (CV)0.7026556
Kurtosis-0.41816017
Mean2201.9296
Median Absolute Deviation (MAD)1021
Skewness0.71418849
Sum1.1864811 × 109
Variance2393822.2
MonotonicityNot monotonic
2024-03-30T02:38:09.738223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
710 313
 
0.1%
568 304
 
0.1%
354 303
 
0.1%
311 300
 
0.1%
1168 295
 
0.1%
2005 289
 
0.1%
366 286
 
0.1%
312 282
 
0.1%
531 281
 
0.1%
2274 279
 
0.1%
Other values (5864) 535905
99.5%
ValueCountFrequency (%)
1 142
< 0.1%
2 185
< 0.1%
3 193
< 0.1%
4 193
< 0.1%
5 84
< 0.1%
6 109
< 0.1%
7 116
< 0.1%
8 94
< 0.1%
9 86
< 0.1%
10 154
< 0.1%
ValueCountFrequency (%)
9887 1
< 0.1%
9768 1
< 0.1%
8819 1
< 0.1%
8818 1
< 0.1%
8815 1
< 0.1%
8812 2
< 0.1%
8810 2
< 0.1%
8809 1
< 0.1%
8808 2
< 0.1%
8807 2
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12653.203
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:10.115155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1524.1973
Coefficient of variation (CV)0.1204594
Kurtosis-1.2937249
Mean12653.203
Median Absolute Deviation (MAD)1591
Skewness0.099030674
Sum6.818014 × 109
Variance2323177.3
MonotonicityNot monotonic
2024-03-30T02:38:10.500256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 26582
 
4.9%
11292 22460
 
4.2%
11298 20502
 
3.8%
13930 20086
 
3.7%
12889 15792
 
2.9%
12892 15424
 
2.9%
11057 15170
 
2.8%
14107 14613
 
2.7%
12953 13479
 
2.5%
13204 13333
 
2.5%
Other values (329) 361396
67.1%
ValueCountFrequency (%)
10135 300
 
0.1%
10136 107
 
< 0.1%
10140 1690
0.3%
10141 61
 
< 0.1%
10146 82
 
< 0.1%
10155 96
 
< 0.1%
10157 142
 
< 0.1%
10158 221
 
< 0.1%
10165 8
 
< 0.1%
10170 50
 
< 0.1%
ValueCountFrequency (%)
16869 107
 
< 0.1%
16218 90
 
< 0.1%
15991 60
 
< 0.1%
15919 866
0.2%
15841 60
 
< 0.1%
15624 473
0.1%
15607 82
 
< 0.1%
15582 51
 
< 0.1%
15569 58
 
< 0.1%
15412 925
0.2%

ORIGIN
Text

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:11.208635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1616511
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBDL
2nd rowDLH
3rd rowORF
4th rowMSP
5th rowPIT
ValueCountFrequency (%)
atl 26582
 
4.9%
den 22460
 
4.2%
dfw 20502
 
3.8%
ord 20086
 
3.7%
las 15792
 
2.9%
lax 15424
 
2.9%
clt 15170
 
2.8%
phx 14613
 
2.7%
lga 13479
 
2.5%
mco 13333
 
2.5%
Other values (329) 361396
67.1%
2024-03-30T02:38:12.240654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 186296
 
11.5%
L 151614
 
9.4%
S 138357
 
8.6%
D 124019
 
7.7%
T 85072
 
5.3%
O 82063
 
5.1%
C 81602
 
5.0%
M 71680
 
4.4%
F 64963
 
4.0%
P 62456
 
3.9%
Other values (16) 568389
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 186296
 
11.5%
L 151614
 
9.4%
S 138357
 
8.6%
D 124019
 
7.7%
T 85072
 
5.3%
O 82063
 
5.1%
C 81602
 
5.0%
M 71680
 
4.4%
F 64963
 
4.0%
P 62456
 
3.9%
Other values (16) 568389
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 186296
 
11.5%
L 151614
 
9.4%
S 138357
 
8.6%
D 124019
 
7.7%
T 85072
 
5.3%
O 82063
 
5.1%
C 81602
 
5.0%
M 71680
 
4.4%
F 64963
 
4.0%
P 62456
 
3.9%
Other values (16) 568389
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 186296
 
11.5%
L 151614
 
9.4%
S 138357
 
8.6%
D 124019
 
7.7%
T 85072
 
5.3%
O 82063
 
5.1%
C 81602
 
5.0%
M 71680
 
4.4%
F 64963
 
4.0%
P 62456
 
3.9%
Other values (16) 568389
35.2%
Distinct333
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:12.782653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.003541
Min length8

Characters and Unicode

Total characters7006789
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHartford, CT
2nd rowDuluth, MN
3rd rowNorfolk, VA
4th rowMinneapolis, MN
5th rowPittsburgh, PA
ValueCountFrequency (%)
ca 58708
 
4.7%
tx 54592
 
4.3%
fl 50713
 
4.0%
ny 30887
 
2.5%
ga 28431
 
2.3%
new 28393
 
2.3%
san 27845
 
2.2%
il 27631
 
2.2%
chicago 26584
 
2.1%
atlanta 26582
 
2.1%
Other values (404) 897785
71.4%
2024-03-30T02:38:13.685538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
719314
 
10.3%
, 538837
 
7.7%
a 535724
 
7.6%
o 383728
 
5.5%
e 372247
 
5.3%
n 343244
 
4.9%
t 330328
 
4.7%
l 303600
 
4.3%
i 263079
 
3.8%
r 252146
 
3.6%
Other values (46) 2964542
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7006789
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
719314
 
10.3%
, 538837
 
7.7%
a 535724
 
7.6%
o 383728
 
5.5%
e 372247
 
5.3%
n 343244
 
4.9%
t 330328
 
4.7%
l 303600
 
4.3%
i 263079
 
3.8%
r 252146
 
3.6%
Other values (46) 2964542
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7006789
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
719314
 
10.3%
, 538837
 
7.7%
a 535724
 
7.6%
o 383728
 
5.5%
e 372247
 
5.3%
n 343244
 
4.9%
t 330328
 
4.7%
l 303600
 
4.3%
i 263079
 
3.8%
r 252146
 
3.6%
Other values (46) 2964542
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7006789
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
719314
 
10.3%
, 538837
 
7.7%
a 535724
 
7.6%
o 383728
 
5.5%
e 372247
 
5.3%
n 343244
 
4.9%
t 330328
 
4.7%
l 303600
 
4.3%
i 263079
 
3.8%
r 252146
 
3.6%
Other values (46) 2964542
42.3%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:14.143942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.137459
Min length4

Characters and Unicode

Total characters4384764
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConnecticut
2nd rowMinnesota
3rd rowVirginia
4th rowMinnesota
5th rowPennsylvania
ValueCountFrequency (%)
california 58708
 
9.5%
texas 54592
 
8.8%
florida 50713
 
8.2%
new 45283
 
7.3%
york 30887
 
5.0%
georgia 28431
 
4.6%
illinois 27631
 
4.5%
colorado 25999
 
4.2%
carolina 25698
 
4.2%
north 23139
 
3.7%
Other values (51) 247358
40.0%
2024-03-30T02:38:14.808891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 588608
13.4%
i 494644
 
11.3%
o 423646
 
9.7%
r 322603
 
7.4%
n 318238
 
7.3%
e 269553
 
6.1%
l 244743
 
5.6%
s 244019
 
5.6%
C 112162
 
2.6%
d 111047
 
2.5%
Other values (37) 1255501
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4384764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 588608
13.4%
i 494644
 
11.3%
o 423646
 
9.7%
r 322603
 
7.4%
n 318238
 
7.3%
e 269553
 
6.1%
l 244743
 
5.6%
s 244019
 
5.6%
C 112162
 
2.6%
d 111047
 
2.5%
Other values (37) 1255501
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4384764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 588608
13.4%
i 494644
 
11.3%
o 423646
 
9.7%
r 322603
 
7.4%
n 318238
 
7.3%
e 269553
 
6.1%
l 244743
 
5.6%
s 244019
 
5.6%
C 112162
 
2.6%
d 111047
 
2.5%
Other values (37) 1255501
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4384764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 588608
13.4%
i 494644
 
11.3%
o 423646
 
9.7%
r 322603
 
7.4%
n 318238
 
7.3%
e 269553
 
6.1%
l 244743
 
5.6%
s 244019
 
5.6%
C 112162
 
2.6%
d 111047
 
2.5%
Other values (37) 1255501
28.6%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.355763
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:15.204467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)49

Descriptive statistics

Standard deviation26.919824
Coefficient of variation (CV)0.49525244
Kurtosis-1.3294453
Mean54.355763
Median Absolute Deviation (MAD)22
Skewness-0.022303038
Sum29288896
Variance724.67692
MonotonicityNot monotonic
2024-03-30T02:38:15.508167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 58708
 
10.9%
74 54592
 
10.1%
33 50713
 
9.4%
22 30887
 
5.7%
34 28431
 
5.3%
41 27631
 
5.1%
82 25999
 
4.8%
36 21736
 
4.0%
38 18636
 
3.5%
85 17395
 
3.2%
Other values (42) 204109
37.9%
ValueCountFrequency (%)
1 2684
 
0.5%
2 11037
2.0%
3 3126
 
0.6%
4 581
 
0.1%
5 104
 
< 0.1%
11 1757
 
0.3%
12 995
 
0.2%
13 11015
2.0%
14 507
 
0.1%
15 1179
 
0.2%
ValueCountFrequency (%)
93 13640
 
2.5%
92 5845
 
1.1%
91 58708
10.9%
88 954
 
0.2%
87 9421
 
1.7%
86 1902
 
0.4%
85 17395
 
3.2%
84 1913
 
0.4%
83 2230
 
0.4%
82 25999
4.8%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12653.175
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:15.833079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1524.1464
Coefficient of variation (CV)0.12045564
Kurtosis-1.2936425
Mean12653.175
Median Absolute Deviation (MAD)1591
Skewness0.099123627
Sum6.817999 × 109
Variance2323022.2
MonotonicityNot monotonic
2024-03-30T02:38:16.343724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 26566
 
4.9%
11292 22461
 
4.2%
11298 20521
 
3.8%
13930 20092
 
3.7%
12889 15793
 
2.9%
12892 15428
 
2.9%
11057 15167
 
2.8%
14107 14620
 
2.7%
12953 13481
 
2.5%
13204 13327
 
2.5%
Other values (329) 361381
67.1%
ValueCountFrequency (%)
10135 298
 
0.1%
10136 109
 
< 0.1%
10140 1689
0.3%
10141 61
 
< 0.1%
10146 82
 
< 0.1%
10155 96
 
< 0.1%
10157 143
 
< 0.1%
10158 220
 
< 0.1%
10165 8
 
< 0.1%
10170 51
 
< 0.1%
ValueCountFrequency (%)
16869 107
 
< 0.1%
16218 90
 
< 0.1%
15991 60
 
< 0.1%
15919 865
0.2%
15841 60
 
< 0.1%
15624 473
0.1%
15607 82
 
< 0.1%
15582 51
 
< 0.1%
15569 59
 
< 0.1%
15412 924
0.2%

DEST
Text

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:17.213849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1616511
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLGA
2nd rowMSP
3rd rowDTW
4th rowPIT
5th rowMSP
ValueCountFrequency (%)
atl 26566
 
4.9%
den 22461
 
4.2%
dfw 20521
 
3.8%
ord 20092
 
3.7%
las 15793
 
2.9%
lax 15428
 
2.9%
clt 15167
 
2.8%
phx 14620
 
2.7%
lga 13481
 
2.5%
mco 13327
 
2.5%
Other values (329) 361381
67.1%
2024-03-30T02:38:18.389372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 186308
 
11.5%
L 151565
 
9.4%
S 138336
 
8.6%
D 124055
 
7.7%
T 85066
 
5.3%
O 82052
 
5.1%
C 81606
 
5.0%
M 71675
 
4.4%
F 64964
 
4.0%
P 62465
 
3.9%
Other values (16) 568419
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 186308
 
11.5%
L 151565
 
9.4%
S 138336
 
8.6%
D 124055
 
7.7%
T 85066
 
5.3%
O 82052
 
5.1%
C 81606
 
5.0%
M 71675
 
4.4%
F 64964
 
4.0%
P 62465
 
3.9%
Other values (16) 568419
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 186308
 
11.5%
L 151565
 
9.4%
S 138336
 
8.6%
D 124055
 
7.7%
T 85066
 
5.3%
O 82052
 
5.1%
C 81606
 
5.0%
M 71675
 
4.4%
F 64964
 
4.0%
P 62465
 
3.9%
Other values (16) 568419
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 186308
 
11.5%
L 151565
 
9.4%
S 138336
 
8.6%
D 124055
 
7.7%
T 85066
 
5.3%
O 82052
 
5.1%
C 81606
 
5.0%
M 71675
 
4.4%
F 64964
 
4.0%
P 62465
 
3.9%
Other values (16) 568419
35.2%
Distinct333
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:18.932382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.003663
Min length8

Characters and Unicode

Total characters7006855
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowMinneapolis, MN
3rd rowDetroit, MI
4th rowPittsburgh, PA
5th rowMinneapolis, MN
ValueCountFrequency (%)
ca 58697
 
4.7%
tx 54615
 
4.3%
fl 50679
 
4.0%
ny 30887
 
2.5%
ga 28415
 
2.3%
new 28395
 
2.3%
san 27819
 
2.2%
il 27635
 
2.2%
chicago 26590
 
2.1%
atlanta 26566
 
2.1%
Other values (404) 897833
71.4%
2024-03-30T02:38:19.768645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
719294
 
10.3%
, 538837
 
7.7%
a 535681
 
7.6%
o 383748
 
5.5%
e 372230
 
5.3%
n 343220
 
4.9%
t 330316
 
4.7%
l 303605
 
4.3%
i 263098
 
3.8%
r 252149
 
3.6%
Other values (46) 2964677
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7006855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
719294
 
10.3%
, 538837
 
7.7%
a 535681
 
7.6%
o 383748
 
5.5%
e 372230
 
5.3%
n 343220
 
4.9%
t 330316
 
4.7%
l 303605
 
4.3%
i 263098
 
3.8%
r 252149
 
3.6%
Other values (46) 2964677
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7006855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
719294
 
10.3%
, 538837
 
7.7%
a 535681
 
7.6%
o 383748
 
5.5%
e 372230
 
5.3%
n 343220
 
4.9%
t 330316
 
4.7%
l 303605
 
4.3%
i 263098
 
3.8%
r 252149
 
3.6%
Other values (46) 2964677
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7006855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
719294
 
10.3%
, 538837
 
7.7%
a 535681
 
7.6%
o 383748
 
5.5%
e 372230
 
5.3%
n 343220
 
4.9%
t 330316
 
4.7%
l 303605
 
4.3%
i 263098
 
3.8%
r 252149
 
3.6%
Other values (46) 2964677
42.3%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:20.251715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.137446
Min length4

Characters and Unicode

Total characters4384757
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowMinnesota
3rd rowMichigan
4th rowPennsylvania
5th rowMinnesota
ValueCountFrequency (%)
california 58697
 
9.5%
texas 54615
 
8.8%
florida 50679
 
8.2%
new 45285
 
7.3%
york 30887
 
5.0%
georgia 28415
 
4.6%
illinois 27635
 
4.5%
colorado 26001
 
4.2%
carolina 25704
 
4.2%
north 23139
 
3.7%
Other values (51) 247385
40.0%
2024-03-30T02:38:20.930193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 588590
13.4%
i 494643
 
11.3%
o 423613
 
9.7%
r 322570
 
7.4%
n 318261
 
7.3%
e 269579
 
6.1%
l 244712
 
5.6%
s 244084
 
5.6%
C 112158
 
2.6%
d 111006
 
2.5%
Other values (37) 1255541
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4384757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 588590
13.4%
i 494643
 
11.3%
o 423613
 
9.7%
r 322570
 
7.4%
n 318261
 
7.3%
e 269579
 
6.1%
l 244712
 
5.6%
s 244084
 
5.6%
C 112158
 
2.6%
d 111006
 
2.5%
Other values (37) 1255541
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4384757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 588590
13.4%
i 494643
 
11.3%
o 423613
 
9.7%
r 322570
 
7.4%
n 318261
 
7.3%
e 269579
 
6.1%
l 244712
 
5.6%
s 244084
 
5.6%
C 112158
 
2.6%
d 111006
 
2.5%
Other values (37) 1255541
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4384757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 588590
13.4%
i 494643
 
11.3%
o 423613
 
9.7%
r 322570
 
7.4%
n 318261
 
7.3%
e 269579
 
6.1%
l 244712
 
5.6%
s 244084
 
5.6%
C 112158
 
2.6%
d 111006
 
2.5%
Other values (37) 1255541
28.6%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.358511
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:21.263396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)49

Descriptive statistics

Standard deviation26.918285
Coefficient of variation (CV)0.49519908
Kurtosis-1.3293716
Mean54.358511
Median Absolute Deviation (MAD)22
Skewness-0.022461321
Sum29290377
Variance724.59406
MonotonicityNot monotonic
2024-03-30T02:38:21.604243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 58697
 
10.9%
74 54615
 
10.1%
33 50679
 
9.4%
22 30887
 
5.7%
34 28415
 
5.3%
41 27635
 
5.1%
82 26001
 
4.8%
36 21735
 
4.0%
38 18651
 
3.5%
85 17393
 
3.2%
Other values (42) 204129
37.9%
ValueCountFrequency (%)
1 2687
 
0.5%
2 11034
2.0%
3 3116
 
0.6%
4 581
 
0.1%
5 104
 
< 0.1%
11 1756
 
0.3%
12 994
 
0.2%
13 11014
2.0%
14 506
 
0.1%
15 1179
 
0.2%
ValueCountFrequency (%)
93 13647
 
2.5%
92 5844
 
1.1%
91 58697
10.9%
88 954
 
0.2%
87 9416
 
1.7%
86 1901
 
0.4%
85 17393
 
3.2%
84 1911
 
0.4%
83 2227
 
0.4%
82 26001
4.8%

CRS_DEP_TIME
Real number (ℝ)

Distinct1212
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1327.4156
Minimum5
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:21.921870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile600
Q1910
median1320
Q31732
95-th percentile2124
Maximum2359
Range2354
Interquartile range (IQR)822

Descriptive statistics

Standard deviation489.90267
Coefficient of variation (CV)0.36906502
Kurtosis-1.0609794
Mean1327.4156
Median Absolute Deviation (MAD)410
Skewness0.087268579
Sum7.1526065 × 108
Variance240004.63
MonotonicityNot monotonic
2024-03-30T02:38:22.295331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 12516
 
2.3%
700 8737
 
1.6%
800 5625
 
1.0%
900 4034
 
0.7%
630 3544
 
0.7%
1000 3535
 
0.7%
830 3298
 
0.6%
1100 3289
 
0.6%
730 3214
 
0.6%
615 3121
 
0.6%
Other values (1202) 487924
90.6%
ValueCountFrequency (%)
5 31
< 0.1%
6 4
 
< 0.1%
9 4
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
14 32
< 0.1%
15 48
< 0.1%
16 6
 
< 0.1%
18 13
 
< 0.1%
19 22
< 0.1%
ValueCountFrequency (%)
2359 750
0.1%
2358 58
 
< 0.1%
2357 25
 
< 0.1%
2356 17
 
< 0.1%
2355 305
0.1%
2354 56
 
< 0.1%
2353 1
 
< 0.1%
2352 35
 
< 0.1%
2351 38
 
< 0.1%
2350 253
 
< 0.1%

DEP_TIME
Real number (ℝ)

MISSING 

Distinct1407
Distinct (%)0.3%
Missing9978
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean1334.4419
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:22.598888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile559
Q1917
median1327
Q31743
95-th percentile2135
Maximum2400
Range2399
Interquartile range (IQR)826

Descriptive statistics

Standard deviation502.42772
Coefficient of variation (CV)0.37650775
Kurtosis-0.99130698
Mean1334.4419
Median Absolute Deviation (MAD)413
Skewness0.033588591
Sum7.057316 × 108
Variance252433.61
MonotonicityNot monotonic
2024-03-30T02:38:22.940899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1492
 
0.3%
556 1304
 
0.2%
557 1225
 
0.2%
554 1196
 
0.2%
558 1134
 
0.2%
559 1098
 
0.2%
553 1090
 
0.2%
600 1023
 
0.2%
552 983
 
0.2%
657 974
 
0.2%
Other values (1397) 517340
96.0%
(Missing) 9978
 
1.9%
ValueCountFrequency (%)
1 66
< 0.1%
2 47
< 0.1%
3 40
< 0.1%
4 50
< 0.1%
5 55
< 0.1%
6 51
< 0.1%
7 39
< 0.1%
8 40
< 0.1%
9 36
< 0.1%
10 36
< 0.1%
ValueCountFrequency (%)
2400 48
 
< 0.1%
2359 87
< 0.1%
2358 86
< 0.1%
2357 82
< 0.1%
2356 85
< 0.1%
2355 92
< 0.1%
2354 100
< 0.1%
2353 88
< 0.1%
2352 121
< 0.1%
2351 104
< 0.1%

DEP_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1110
Distinct (%)0.2%
Missing9982
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean12.937247
Minimum-52
Maximum3024
Zeros23846
Zeros (%)4.4%
Negative302230
Negative (%)56.1%
Memory size4.1 MiB
2024-03-30T02:38:23.251495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-52
5-th percentile-10
Q1-6
median-2
Q310
95-th percentile85
Maximum3024
Range3076
Interquartile range (IQR)16

Descriptive statistics

Standard deviation55.435948
Coefficient of variation (CV)4.2849878
Kurtosis223.07317
Mean12.937247
Median Absolute Deviation (MAD)5
Skewness11.022176
Sum6841928
Variance3073.1443
MonotonicityNot monotonic
2024-03-30T02:38:23.565311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 38269
 
7.1%
-4 35123
 
6.5%
-3 34216
 
6.3%
-2 30667
 
5.7%
-6 30519
 
5.7%
-1 27820
 
5.2%
-7 26264
 
4.9%
0 23846
 
4.4%
-8 21797
 
4.0%
-9 16909
 
3.1%
Other values (1100) 243425
45.2%
ValueCountFrequency (%)
-52 1
 
< 0.1%
-48 1
 
< 0.1%
-45 1
 
< 0.1%
-44 1
 
< 0.1%
-42 2
< 0.1%
-40 3
< 0.1%
-39 1
 
< 0.1%
-38 1
 
< 0.1%
-37 3
< 0.1%
-36 3
< 0.1%
ValueCountFrequency (%)
3024 1
< 0.1%
2669 1
< 0.1%
2554 1
< 0.1%
2250 1
< 0.1%
2168 1
< 0.1%
2130 1
< 0.1%
2047 1
< 0.1%
2036 1
< 0.1%
1933 1
< 0.1%
1875 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct171
Distinct (%)< 0.1%
Missing10197
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean18.334566
Minimum1
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:23.904167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q321
95-th percentile38
Maximum222
Range221
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.62446
Coefficient of variation (CV)0.57947704
Kurtosis17.85648
Mean18.334566
Median Absolute Deviation (MAD)4
Skewness3.1095996
Sum9692385
Variance112.87915
MonotonicityNot monotonic
2024-03-30T02:38:24.397091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 40421
 
7.5%
12 40331
 
7.5%
14 37888
 
7.0%
11 37538
 
7.0%
15 34404
 
6.4%
10 30907
 
5.7%
16 30311
 
5.6%
17 26755
 
5.0%
18 23214
 
4.3%
9 22407
 
4.2%
Other values (161) 204464
37.9%
ValueCountFrequency (%)
1 11
 
< 0.1%
2 17
 
< 0.1%
3 52
 
< 0.1%
4 213
 
< 0.1%
5 635
 
0.1%
6 2803
 
0.5%
7 7093
 
1.3%
8 13524
2.5%
9 22407
4.2%
10 30907
5.7%
ValueCountFrequency (%)
222 1
 
< 0.1%
183 1
 
< 0.1%
180 1
 
< 0.1%
179 1
 
< 0.1%
175 1
 
< 0.1%
172 5
< 0.1%
170 2
 
< 0.1%
169 1
 
< 0.1%
165 1
 
< 0.1%
164 2
 
< 0.1%

TAXI_IN
Real number (ℝ)

MISSING 

Distinct134
Distinct (%)< 0.1%
Missing10519
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean8.0379998
Minimum1
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:24.694252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile19
Maximum173
Range172
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.4165741
Coefficient of variation (CV)0.79827995
Kurtosis35.166111
Mean8.0379998
Median Absolute Deviation (MAD)2
Skewness4.2395693
Sum4246620
Variance41.172423
MonotonicityNot monotonic
2024-03-30T02:38:25.051696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 79279
14.7%
5 75753
14.1%
6 63092
11.7%
7 50739
9.4%
3 48275
9.0%
8 39266
7.3%
9 30738
 
5.7%
10 24016
 
4.5%
11 18890
 
3.5%
12 14646
 
2.7%
Other values (124) 83624
15.5%
ValueCountFrequency (%)
1 689
 
0.1%
2 11397
 
2.1%
3 48275
9.0%
4 79279
14.7%
5 75753
14.1%
6 63092
11.7%
7 50739
9.4%
8 39266
7.3%
9 30738
 
5.7%
10 24016
 
4.5%
ValueCountFrequency (%)
173 1
 
< 0.1%
163 1
 
< 0.1%
152 2
< 0.1%
150 1
 
< 0.1%
147 1
 
< 0.1%
142 1
 
< 0.1%
139 4
< 0.1%
138 1
 
< 0.1%
137 1
 
< 0.1%
136 1
 
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1305
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1497.1776
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:25.399766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile728
Q11109
median1520
Q31927
95-th percentile2257
Maximum2359
Range2358
Interquartile range (IQR)818

Descriptive statistics

Standard deviation515.23676
Coefficient of variation (CV)0.3441387
Kurtosis-0.47236634
Mean1497.1776
Median Absolute Deviation (MAD)410
Skewness-0.28269901
Sum8.0673471 × 108
Variance265468.92
MonotonicityNot monotonic
2024-03-30T02:38:25.698396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 2259
 
0.4%
1810 1863
 
0.3%
1915 1704
 
0.3%
1750 1687
 
0.3%
2000 1574
 
0.3%
1030 1553
 
0.3%
1025 1544
 
0.3%
1640 1539
 
0.3%
1620 1535
 
0.3%
1000 1510
 
0.3%
Other values (1295) 522069
96.9%
ValueCountFrequency (%)
1 76
 
< 0.1%
2 50
 
< 0.1%
3 141
 
< 0.1%
4 105
 
< 0.1%
5 549
0.1%
6 91
 
< 0.1%
7 107
 
< 0.1%
8 181
 
< 0.1%
9 134
 
< 0.1%
10 334
0.1%
ValueCountFrequency (%)
2359 2259
0.4%
2358 552
 
0.1%
2357 496
 
0.1%
2356 505
 
0.1%
2355 1001
0.2%
2354 379
 
0.1%
2353 470
 
0.1%
2352 312
 
0.1%
2351 274
 
0.1%
2350 681
 
0.1%

ARR_TIME
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.3%
Missing10519
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1475.3177
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:26.043533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile656
Q11057
median1512
Q31922
95-th percentile2254
Maximum2400
Range2399
Interquartile range (IQR)865

Descriptive statistics

Standard deviation537.43003
Coefficient of variation (CV)0.36428088
Kurtosis-0.32480644
Mean1475.3177
Median Absolute Deviation (MAD)412
Skewness-0.38509807
Sum7.794369 × 108
Variance288831.04
MonotonicityNot monotonic
2024-03-30T02:38:26.418224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1150 606
 
0.1%
1647 604
 
0.1%
1233 597
 
0.1%
1152 590
 
0.1%
1158 587
 
0.1%
1854 586
 
0.1%
1642 584
 
0.1%
1544 583
 
0.1%
1155 582
 
0.1%
1840 581
 
0.1%
Other values (1430) 522418
97.0%
(Missing) 10519
 
2.0%
ValueCountFrequency (%)
1 341
0.1%
2 266
< 0.1%
3 293
0.1%
4 283
0.1%
5 304
0.1%
6 282
0.1%
7 268
< 0.1%
8 285
0.1%
9 236
< 0.1%
10 264
< 0.1%
ValueCountFrequency (%)
2400 281
0.1%
2359 302
0.1%
2358 352
0.1%
2357 340
0.1%
2356 366
0.1%
2355 323
0.1%
2354 378
0.1%
2353 390
0.1%
2352 361
0.1%
2351 366
0.1%

ARR_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1136
Distinct (%)0.2%
Missing11640
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean7.7763929
Minimum-80
Maximum3063
Zeros9345
Zeros (%)1.7%
Negative315277
Negative (%)58.5%
Memory size4.1 MiB
2024-03-30T02:38:26.718216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-80
5-th percentile-28
Q1-15
median-5
Q311
95-th percentile85
Maximum3063
Range3143
Interquartile range (IQR)26

Descriptive statistics

Standard deviation57.396811
Coefficient of variation (CV)7.3809042
Kurtosis190.85869
Mean7.7763929
Median Absolute Deviation (MAD)12
Skewness9.8552167
Sum4099691
Variance3294.3939
MonotonicityNot monotonic
2024-03-30T02:38:27.056662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12 13860
 
2.6%
-11 13830
 
2.6%
-10 13768
 
2.6%
-9 13384
 
2.5%
-13 13312
 
2.5%
-8 13304
 
2.5%
-14 13009
 
2.4%
-7 12944
 
2.4%
-6 12538
 
2.3%
-15 12515
 
2.3%
Other values (1126) 394733
73.3%
ValueCountFrequency (%)
-80 1
 
< 0.1%
-78 1
 
< 0.1%
-74 2
 
< 0.1%
-73 1
 
< 0.1%
-72 3
< 0.1%
-69 3
< 0.1%
-68 2
 
< 0.1%
-67 6
< 0.1%
-66 6
< 0.1%
-65 6
< 0.1%
ValueCountFrequency (%)
3063 1
< 0.1%
2687 1
< 0.1%
2557 1
< 0.1%
2229 1
< 0.1%
2078 1
< 0.1%
2061 1
< 0.1%
2027 1
< 0.1%
1927 1
< 0.1%
1877 1
< 0.1%
1775 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0.0
528542 
1.0
 
10295

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1616511
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 528542
98.1%
1.0 10295
 
1.9%

Length

2024-03-30T02:38:27.368920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:38:27.590276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 528542
98.1%
1.0 10295
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 1067379
66.0%
. 538837
33.3%
1 10295
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1067379
66.0%
. 538837
33.3%
1 10295
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1067379
66.0%
. 538837
33.3%
1 10295
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1067379
66.0%
. 538837
33.3%
1 10295
 
0.6%

CANCELLATION_CODE
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing528542
Missing (%)98.1%
Memory size4.1 MiB
B
6611 
C
1792 
A
1707 
D
 
185

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10295
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
B 6611
 
1.2%
C 1792
 
0.3%
A 1707
 
0.3%
D 185
 
< 0.1%
(Missing) 528542
98.1%

Length

2024-03-30T02:38:27.897553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:38:28.338237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 6611
64.2%
c 1792
 
17.4%
a 1707
 
16.6%
d 185
 
1.8%

Most occurring characters

ValueCountFrequency (%)
B 6611
64.2%
C 1792
 
17.4%
A 1707
 
16.6%
D 185
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 6611
64.2%
C 1792
 
17.4%
A 1707
 
16.6%
D 185
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 6611
64.2%
C 1792
 
17.4%
A 1707
 
16.6%
D 185
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 6611
64.2%
C 1792
 
17.4%
A 1707
 
16.6%
D 185
 
1.8%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0.0
537492 
1.0
 
1345

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1616511
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 537492
99.8%
1.0 1345
 
0.2%

Length

2024-03-30T02:38:28.711160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:38:28.940300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 537492
99.8%
1.0 1345
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1076329
66.6%
. 538837
33.3%
1 1345
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1076329
66.6%
. 538837
33.3%
1 1345
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1076329
66.6%
. 538837
33.3%
1 1345
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1616511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1076329
66.6%
. 538837
33.3%
1 1345
 
0.1%

AIR_TIME
Real number (ℝ)

MISSING 

Distinct642
Distinct (%)0.1%
Missing11640
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean115.81437
Minimum8
Maximum695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:29.195450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile36
Q163
median99
Q3146
95-th percentile271
Maximum695
Range687
Interquartile range (IQR)83

Descriptive statistics

Standard deviation71.811305
Coefficient of variation (CV)0.62005525
Kurtosis2.9615396
Mean115.81437
Median Absolute Deviation (MAD)40
Skewness1.48244
Sum61056986
Variance5156.8635
MonotonicityNot monotonic
2024-03-30T02:38:29.648728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 4435
 
0.8%
65 4416
 
0.8%
62 4404
 
0.8%
61 4401
 
0.8%
64 4401
 
0.8%
60 4396
 
0.8%
58 4366
 
0.8%
66 4295
 
0.8%
57 4267
 
0.8%
59 4251
 
0.8%
Other values (632) 483565
89.7%
(Missing) 11640
 
2.2%
ValueCountFrequency (%)
8 4
 
< 0.1%
9 16
 
< 0.1%
10 12
 
< 0.1%
11 7
 
< 0.1%
12 3
 
< 0.1%
13 6
 
< 0.1%
14 7
 
< 0.1%
15 30
 
< 0.1%
16 99
< 0.1%
17 155
< 0.1%
ValueCountFrequency (%)
695 1
< 0.1%
694 1
< 0.1%
688 1
< 0.1%
684 1
< 0.1%
677 1
< 0.1%
675 1
< 0.1%
673 1
< 0.1%
668 1
< 0.1%
666 1
< 0.1%
665 1
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct850
Distinct (%)0.7%
Missing422124
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean24.509343
Minimum0
Maximum3024
Zeros53559
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:29.988231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q323
95-th percentile102.4
Maximum3024
Range3024
Interquartile range (IQR)23

Descriptive statistics

Standard deviation73.732581
Coefficient of variation (CV)3.0083458
Kurtosis188.46874
Mean24.509343
Median Absolute Deviation (MAD)3
Skewness10.817922
Sum2860559
Variance5436.4935
MonotonicityNot monotonic
2024-03-30T02:38:30.348926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53559
 
9.9%
15 1941
 
0.4%
2 1775
 
0.3%
6 1774
 
0.3%
16 1747
 
0.3%
3 1744
 
0.3%
1 1716
 
0.3%
7 1694
 
0.3%
17 1652
 
0.3%
4 1632
 
0.3%
Other values (840) 47479
 
8.8%
(Missing) 422124
78.3%
ValueCountFrequency (%)
0 53559
9.9%
1 1716
 
0.3%
2 1775
 
0.3%
3 1744
 
0.3%
4 1632
 
0.3%
5 1613
 
0.3%
6 1774
 
0.3%
7 1694
 
0.3%
8 1611
 
0.3%
9 1532
 
0.3%
ValueCountFrequency (%)
3024 1
< 0.1%
2669 1
< 0.1%
2554 1
< 0.1%
2229 1
< 0.1%
2078 1
< 0.1%
2047 1
< 0.1%
1875 1
< 0.1%
1807 1
< 0.1%
1775 1
< 0.1%
1740 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct537
Distinct (%)0.5%
Missing422124
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean4.3839675
Minimum0
Maximum1653
Zeros110206
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:30.703327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum1653
Range1653
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38.069314
Coefficient of variation (CV)8.6837583
Kurtosis448.4417
Mean4.3839675
Median Absolute Deviation (MAD)0
Skewness18.389964
Sum511666
Variance1449.2727
MonotonicityNot monotonic
2024-03-30T02:38:31.024924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 110206
 
20.5%
15 132
 
< 0.1%
17 130
 
< 0.1%
20 123
 
< 0.1%
19 121
 
< 0.1%
16 116
 
< 0.1%
7 113
 
< 0.1%
6 112
 
< 0.1%
8 112
 
< 0.1%
9 109
 
< 0.1%
Other values (527) 5439
 
1.0%
(Missing) 422124
78.3%
ValueCountFrequency (%)
0 110206
20.5%
1 104
 
< 0.1%
2 94
 
< 0.1%
3 96
 
< 0.1%
4 95
 
< 0.1%
5 88
 
< 0.1%
6 112
 
< 0.1%
7 113
 
< 0.1%
8 112
 
< 0.1%
9 109
 
< 0.1%
ValueCountFrequency (%)
1653 1
< 0.1%
1496 1
< 0.1%
1482 1
< 0.1%
1418 1
< 0.1%
1415 1
< 0.1%
1397 1
< 0.1%
1369 1
< 0.1%
1364 1
< 0.1%
1217 1
< 0.1%
1163 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct397
Distinct (%)0.3%
Missing422124
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean14.648488
Minimum0
Maximum1343
Zeros57001
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:31.341408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q318
95-th percentile62
Maximum1343
Range1343
Interquartile range (IQR)18

Descriptive statistics

Standard deviation32.531918
Coefficient of variation (CV)2.2208379
Kurtosis135.7771
Mean14.648488
Median Absolute Deviation (MAD)1
Skewness7.751564
Sum1709669
Variance1058.3257
MonotonicityNot monotonic
2024-03-30T02:38:31.668464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57001
 
10.6%
1 2825
 
0.5%
15 2353
 
0.4%
2 2080
 
0.4%
16 2065
 
0.4%
17 1962
 
0.4%
3 1896
 
0.4%
4 1783
 
0.3%
18 1738
 
0.3%
5 1717
 
0.3%
Other values (387) 41293
 
7.7%
(Missing) 422124
78.3%
ValueCountFrequency (%)
0 57001
10.6%
1 2825
 
0.5%
2 2080
 
0.4%
3 1896
 
0.4%
4 1783
 
0.3%
5 1717
 
0.3%
6 1593
 
0.3%
7 1615
 
0.3%
8 1516
 
0.3%
9 1443
 
0.3%
ValueCountFrequency (%)
1343 1
< 0.1%
1079 1
< 0.1%
1060 1
< 0.1%
1005 1
< 0.1%
968 1
< 0.1%
931 1
< 0.1%
926 1
< 0.1%
894 1
< 0.1%
867 1
< 0.1%
844 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct96
Distinct (%)0.1%
Missing422124
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean0.14624763
Minimum0
Maximum234
Zeros116087
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:32.023226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum234
Range234
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8958323
Coefficient of variation (CV)19.800883
Kurtosis1658.0491
Mean0.14624763
Median Absolute Deviation (MAD)0
Skewness34.448786
Sum17069
Variance8.3858446
MonotonicityNot monotonic
2024-03-30T02:38:32.367395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116087
 
21.5%
6 27
 
< 0.1%
15 26
 
< 0.1%
18 25
 
< 0.1%
12 21
 
< 0.1%
10 21
 
< 0.1%
16 20
 
< 0.1%
8 20
 
< 0.1%
19 20
 
< 0.1%
20 18
 
< 0.1%
Other values (86) 428
 
0.1%
(Missing) 422124
78.3%
ValueCountFrequency (%)
0 116087
21.5%
1 15
 
< 0.1%
2 12
 
< 0.1%
3 10
 
< 0.1%
4 11
 
< 0.1%
5 18
 
< 0.1%
6 27
 
< 0.1%
7 18
 
< 0.1%
8 20
 
< 0.1%
9 15
 
< 0.1%
ValueCountFrequency (%)
234 1
< 0.1%
197 1
< 0.1%
192 1
< 0.1%
189 1
< 0.1%
168 2
< 0.1%
155 1
< 0.1%
139 1
< 0.1%
132 2
< 0.1%
122 1
< 0.1%
121 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct615
Distinct (%)0.5%
Missing422124
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean25.755743
Minimum0
Maximum2027
Zeros62630
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-03-30T02:38:32.683689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q329
95-th percentile122
Maximum2027
Range2027
Interquartile range (IQR)29

Descriptive statistics

Standard deviation57.507827
Coefficient of variation (CV)2.2328157
Kurtosis102.89642
Mean25.755743
Median Absolute Deviation (MAD)0
Skewness7.0764072
Sum3006030
Variance3307.1501
MonotonicityNot monotonic
2024-03-30T02:38:33.052214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62630
 
11.6%
15 1210
 
0.2%
16 1172
 
0.2%
17 1149
 
0.2%
18 1107
 
0.2%
19 1063
 
0.2%
20 1008
 
0.2%
21 966
 
0.2%
14 909
 
0.2%
13 907
 
0.2%
Other values (605) 44592
 
8.3%
(Missing) 422124
78.3%
ValueCountFrequency (%)
0 62630
11.6%
1 721
 
0.1%
2 754
 
0.1%
3 727
 
0.1%
4 713
 
0.1%
5 728
 
0.1%
6 770
 
0.1%
7 749
 
0.1%
8 822
 
0.2%
9 782
 
0.1%
ValueCountFrequency (%)
2027 1
< 0.1%
1752 1
< 0.1%
1560 1
< 0.1%
1486 1
< 0.1%
1439 1
< 0.1%
1397 1
< 0.1%
1363 1
< 0.1%
1309 1
< 0.1%
1216 1
< 0.1%
1211 1
< 0.1%

Interactions

2024-03-30T02:37:50.774787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:41.648723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:47.913932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:53.925372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:01.287334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:08.413450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:16.189963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:23.131091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:30.660940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:37.749610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:45.173496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:52.019112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:59.234795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:05.692681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:13.393101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:20.497998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:27.479036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:32.938072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:38.595655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:44.398679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:51.067738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:42.030250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:48.224637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:54.287889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:01.631064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:08.801474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:16.521424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:23.534106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:30.985493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:38.154598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:45.498713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:52.441880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:59.589696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:06.121589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:13.782474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:20.896300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:27.732453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:33.189823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:38.853540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:45.569959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:51.320285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:42.362194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:48.513238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:54.600714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:01.962574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:09.487066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:16.993108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:23.900661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:31.312193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:38.564558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:45.833000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:52.845500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:59.914552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:06.596195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:14.213951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:21.268341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:27.995654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:33.467039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:39.086016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:45.895800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:51.622952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:42.692306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:48.789624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:54.968091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:02.326622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:09.979298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:17.342728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:24.285397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:31.993559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:39.120470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:46.231543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:53.282898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:00.327531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:06.926478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:14.622360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:21.672963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:28.309058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:33.746100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:39.396623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:46.242680image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:51.891636image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:42.999281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:49.048731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:55.312216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:02.621539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:10.354996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:17.648305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:24.620741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:32.403839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:39.495322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:46.561481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:53.668874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:00.649787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:07.288028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:14.963460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:22.065430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:28.559173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:34.038524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:39.662712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:46.485455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:52.222863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:43.321471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:49.326887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:55.732982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:02.945322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:10.789852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:17.990240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:24.978375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:32.750927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:39.891307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:46.902916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:54.118424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:00.981205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:07.682533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:15.373838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:22.473133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:28.828575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:34.372218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:39.949113image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:46.792296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:52.496871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:43.624188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:49.618174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:56.098635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:03.231396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:11.163352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:18.323219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:25.269892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:33.055941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:40.321721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:47.220833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:54.508943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:01.297393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:08.072413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T02:37:12.064789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:19.103318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:26.391605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:31.761576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:37.502368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:42.987649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:49.626799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:55.736619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:47.047602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:53.027148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:00.326468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:07.332923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:15.128659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:22.069249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:29.688624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:36.846831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:44.250288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:50.999018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:58.195102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:04.827966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:12.397452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:19.359725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:26.661341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:32.040741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:37.758502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:43.214044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:49.916396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:56.026965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:47.281975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:53.298759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:00.617130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:07.645013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:15.448353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:22.400352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:29.975391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:37.097394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:44.530832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:51.316662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:58.489092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:05.089749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:12.689322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:19.653937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:26.939710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:32.357045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:38.073612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:43.486887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:50.216559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:56.298724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:47.493173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:35:53.551444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:00.870631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:07.935997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:15.721734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:22.667593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:30.264093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:37.350203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:44.791115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:51.569058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:36:58.722869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:05.329360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:12.976320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:20.042104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:27.186169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:32.648961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:38.327869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:43.707227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:37:50.495897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T02:37:57.060195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T02:38:00.339655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
011/2/2023 12:00:00 AM9E462810529BDLHartford, CTConnecticut1112953LGANew York, NYNew York22800757.0-3.011.020.0905853.0-12.00.0NaN0.025.0NaNNaNNaNNaNNaN
111/2/2023 12:00:00 AM9E463011337DLHDuluth, MNMinnesota6313487MSPMinneapolis, MNMinnesota63510502.0-8.029.015.0626622.0-4.00.0NaN0.036.0NaNNaNNaNNaNNaN
211/2/2023 12:00:00 AM9E463313931ORFNorfolk, VAVirginia3811433DTWDetroit, MIMichigan43530526.0-4.020.08.0735728.0-7.00.0NaN0.094.0NaNNaNNaNNaNNaN
311/2/2023 12:00:00 AM9E463413487MSPMinneapolis, MNMinnesota6314122PITPittsburgh, PAPennsylvania23910911.01.036.04.012201219.0-1.00.0NaN0.088.0NaNNaNNaNNaNNaN
411/2/2023 12:00:00 AM9E463414122PITPittsburgh, PAPennsylvania2313487MSPMinneapolis, MNMinnesota6313101305.0-5.016.024.014401446.06.00.0NaN0.0121.0NaNNaNNaNNaNNaN
511/2/2023 12:00:00 AM9E463910397ATLAtlanta, GAGeorgia3412323ILMWilmington, NCNorth Carolina36855852.0-3.020.04.010201051.031.00.0NaN0.095.00.00.031.00.00.0
611/2/2023 12:00:00 AM9E463912323ILMWilmington, NCNorth Carolina3610397ATLAtlanta, GAGeorgia3411001127.027.012.011.012411254.013.00.0NaN0.064.0NaNNaNNaNNaNNaN
711/2/2023 12:00:00 AM9E464011003CIDCedar Rapids/Iowa City, IAIowa6113487MSPMinneapolis, MNMinnesota63630634.04.024.09.0803752.0-11.00.0NaN0.045.0NaNNaNNaNNaNNaN
811/2/2023 12:00:00 AM9E464210397ATLAtlanta, GAGeorgia3410990CHOCharlottesville, VAVirginia3814301422.0-8.014.04.016041549.0-15.00.0NaN0.069.0NaNNaNNaNNaNNaN
911/2/2023 12:00:00 AM9E464612478JFKNew York, NYNew York2214685SAVSavannah, GAGeorgia3415301545.015.026.05.018021801.0-1.00.0NaN0.0105.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
53882771/29/2023 12:00:00 AMYX585511278DCAWashington, DCVirginia3810721BOSBoston, MAMassachusetts1311301124.0-6.015.06.013011251.0-10.00.0NaN0.066.0NaNNaNNaNNaNNaN
53882871/29/2023 12:00:00 AMYX585612953LGANew York, NYNew York2213931ORFNorfolk, VAVirginia3810551049.0-6.019.06.012331215.0-18.00.0NaN0.061.0NaNNaNNaNNaNNaN
53882971/29/2023 12:00:00 AMYX585712953LGANew York, NYNew York2211042CLECleveland, OHOhio4417101709.0-1.038.011.019071921.014.00.0NaN0.083.0NaNNaNNaNNaNNaN
53883071/29/2023 12:00:00 AMYX585810721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3812151208.0-7.020.04.014081358.0-10.00.0NaN0.086.0NaNNaNNaNNaNNaN
53883171/29/2023 12:00:00 AMYX585914730SDFLouisville, KYKentucky5211433DTWDetroit, MIMichigan43735728.0-7.012.024.0910853.0-17.00.0NaN0.049.0NaNNaNNaNNaNNaN
53883271/29/2023 12:00:00 AMYX586012339INDIndianapolis, INIndiana4212953LGANew York, NYNew York22955946.0-9.013.010.012041136.0-28.00.0NaN0.087.0NaNNaNNaNNaNNaN
53883371/29/2023 12:00:00 AMYX586110721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3810151006.0-9.018.03.012111145.0-26.00.0NaN0.078.0NaNNaNNaNNaNNaN
53883471/29/2023 12:00:00 AMYX586111278DCAWashington, DCVirginia3810721BOSBoston, MAMassachusetts1312591250.0-9.013.012.014401418.0-22.00.0NaN0.063.0NaNNaNNaNNaNNaN
53883571/29/2023 12:00:00 AMYX586210785BTVBurlington, VTVermont1612953LGANew York, NYNew York2211151109.0-6.020.010.012391233.0-6.00.0NaN0.054.0NaNNaNNaNNaNNaN
53883671/29/2023 12:00:00 AMYX586312478JFKNew York, NYNew York2214321PWMPortland, MEMaine1213301335.05.019.06.014531444.0-9.00.0NaN0.044.0NaNNaNNaNNaNNaN